Solutions
Built for the pipelines nobody else covers
The data-observability category assumes Snowflake, dbt and Fivetran. A great deal of the world's ETL is a .NET service writing to SQL Server on a schedule, and it is completely invisible to those tools.
By stack
SQL Server & SSIS
Agent jobs in msdb, .dtsx packages, parameters in SSISDB, and stored procedures that exist in no repository.
ReadCustom ETL services
Bespoke .NET, Java and Python pipelines. Queues, batch files, mappers, bulk inserts.
ReadAirflow & dbt
Modern stacks get the same reconciliation — and the same honesty about what the DAG does not describe.
ReadBy failure mode
The shapes that recur, and the evidence that identifies each one.
Silent data loss
The run reported success and diverted rows to a table nobody monitors. Nothing threw, so nothing alerted.
Duplicate rows
A retry re-ran a step that had already committed. The duplicate count matches the retried batch exactly.
Missing upstream input
The source never delivered. The pipeline short-circuited and exited clean.
Contention and deadlocks
Two jobs owned by different teams write to the same table in the same window, and neither definition mentions the other.
Late arrival and SLA breach
Row counts correct, timing wrong. Alerting on failure alone never catches it.
Undeclared configuration
The same commit behaves differently on different hosts, so the diff explains nothing.
By role
Data engineers
Stop reconstructing the same investigation by hand. The hypotheses already ruled out are shown with their disproof.
Platform leads
Coverage per pipeline, drift findings, and a diagnosis accuracy figure measured against what your team confirmed.
Security and compliance
An approved query catalogue, redaction at the agent, scoped access, and an audit trail for every action.
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Bring us a pipeline that broke last week
The fastest way to evaluate this is a real incident you already know the answer to. If Decim gets it wrong, that is a far more useful demo than one where it doesn't.